Wind turbine farms have become increasingly popular as sources of renewable energy. Because the capital costs associated with establishing and operating a wind farm can be high, careful planning is needed to select an optimal location. Flat open spaces or elevated areas such as ridges might initially seem to be good potential wind farm sites, but localized atmospheric conditions and uneven wind patterns can reduce the output and therefore the viability of particular sites.
Developers therefore typically evaluate potential sites by studying wind patterns and other meteorological conditions over time. A single test turbine or tower is often set up on a site and measurements are collected over a relatively short time period, for example for one to several years. The measurements can then be analyzed to determine the viability of the site for a permanent wind farm. Meteorological measurements at nearby locations, such as airports, weather service, or other collection points, are typically publicly available and have usually been collected for decades or more, and therefore may also be analyzed with respect to potential wind farm sites.
The analysis of meteorological measurements collected on-site by a test tower or nearby from an airport or other collector, can be carried out in a multitude of ways. On-site test tower data is usually most accurate but is only available for the test period, which is relatively short and may not capture longer term meteorological trends at the test site. For example, a developer of a wind project may have collected one or a few years of wind speed and wind direction measurements on a property. Because wind characteristics can change considerably from one year to the next, and may have multiyear or decadal patterns that cannot be captured in a relatively short period of on-site measurement, it is important to find a way to estimate the longer-term wind characteristics over decades of time. If only nearby data and not on-site test data is available, a developer can extrapolate the data to estimate conditions at the particular proposed location. Although such methods often provide longer term data collected over several decades, extrapolating the data to another location introduces uncertainties and usually provides less accurate results.
A more common and typically more accurate analysis approach combines the two above-described data sets in a Measure-Correlate-Predict (MCP) methodology. As depicted in FIG. 1, a traditional MCP approach uses linear correlation between measurements collected at a single nearby off-site long-term wind measurement point 104 and a shorter-term on-site measurement point 102. In particular, one linear MCP approach uses data from a single long-term measurement point 104 (such as an airport anemometer measurement dataset), finds a relationship between long-term measurement point 104 and on-site measurement point 102 using a simple method such as linear correlation (the “correlate” step), and then uses this relationship to estimate on-site values for point 102 using long-term measurement point 104 for the longer time period (the time period for which only the long-term values, and not the on-site values, are available). In some cases, this may be done on a “sectorized” basis using wind direction and/or wind speed bins and determining a correlation for each bin of values, and different time averaging periods can be used for the analysis (daily, weekly or monthly average wind speeds, for example), but the process remains basically the same. One example of a wind direction bin would be to divide the 360 degrees of the compass into twelve sectors of thirty degrees each, allocate the wind data to these bins based on its direction, and then correlate each bin separately, but many variations are possible.
One example of an existing methodology is U.S. Pat. No. 6,975,925, directed to forecasting an energy output of a wind farm. Other general examples of predictive modeling include U.S. Patent Application Publication No. 2005/0234762, which is directed to dimension reduction in predictive model development, and U.S. Patent Application Publication No. 2003/0160457, directed to a method and device for processing and predicting the flow parameters of turbulent media. Some existing systems and methods particularly relate to insurance and risk evaluation, such as U.S. Patent Application Publication No. 2002/0194113, directed to a system, method, and computer program product for risk-minimization and mutual insurance relations in meteorology dependent activities, and U.S. Patent Application Publication No. 2005/0108150, directed to a method and system for creating wind index values supporting the settlement of risk transfer and derivative contracts.
These and other traditional approaches suffer from a number of drawbacks. In particular, conventional methodologies utilize only a limited data set over a limited time frame. Further, traditional MCP systems do not use more complex data sets, including multiple variable three-dimensional data collected at multiple points and locations. Therefore, traditional linear MCP systems have been acceptable but provide results constrained by the limited data sets. As interest and investment in renewable wind energy sources recently has increased, the marginality of results has become more important in the industry, requiring better, more refined models. Accordingly, a need exists for new, more robust MCP systems and methods capable of handling larger real and predictive data sets and information to provide accurate results.